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Journal of Neurotherapy: Investigations in Neuromodulation, Neurofeedback and Applied Neuroscience Induced EEG Gamma Oscillation Alignment Improves Differentiation Between Autism and ADHD Group Responses in a Facial Categorization Task Eric Gross a , Ayman S. El-Baz a , Guela E. Sokhadze a , Lonnie Sears b , Manuel F. Casanova a c a c & Estate M. Sokhadze a Department of Bioengineering , University of Louisville , Louisville , Kentucky , USA b Department of Pediatrics , University of Louisville School of Medicine , Louisville , Kentucky , USA c Department of Psychiatry and Behavioral Sciences , University of Louisville School of Medicine , Louisville , Kentucky , USA Published online: 29 May 2012. To cite this article: Eric Gross , Ayman S. El-Baz , Guela E. Sokhadze , Lonnie Sears , Manuel F. Casanova & Estate M. Sokhadze (2012) Induced EEG Gamma Oscillation Alignment Improves Differentiation Between Autism and ADHD Group Responses in a Facial Categorization Task, Journal of Neurotherapy: Investigations in Neuromodulation, Neurofeedback and Applied Neuroscience, 16:2, 78-91, DOI: 10.1080/10874208.2012.677631 To link to this article: http://dx.doi.org/10.1080/10874208.2012.677631 PLEASE SCROLL DOWN FOR ARTICLE © International Society for Neurofeedback and Research (ISNR), all rights reserved. This article (the “Article”) may be accessed online from ISNR at no charge. The Article may be viewed online, stored in electronic or physical form, or archived for research, teaching, and private study purposes. The Article may be archived in public libraries or university libraries at the direction of said public library or university library. 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ISSN: 1087-4208 print=1530-017X online DOI: 10.1080/10874208.2012.677631 SCIENTIFIC FEATURES INDUCED EEG GAMMA OSCILLATION ALIGNMENT IMPROVES DIFFERENTIATION BETWEEN AUTISM AND ADHD GROUP RESPONSES IN A FACIAL CATEGORIZATION TASK Eric Gross1, Ayman S. El-Baz1, Guela E. Sokhadze1, Lonnie Sears2, Manuel F. Casanova1,3, Estate M. Sokhadze1,3 1Department of Bioengineering, University of Louisville, Louisville, Kentucky, USA 2Department of Pediatrics, University of Louisville School of Medicine, Louisville, Kentucky, USA 3Department of Psychiatry and Behavioral Sciences, University of Louisville School of Medicine, Louisville, Kentucky, USA Children diagnosed with an autism spectrum disorder (ASD) often lack the ability to recog- nize and properly respond to emotional stimuli. Emotional deficits also characterize chil- dren with attention deficit/hyperactivity disorder (ADHD), in addition to exhibiting limited attention span. These abnormalities may effect a difference in the induced EEG gamma wave burst (35–45 Hz) peaked approximately 300–400 ms following an emotional stimulus. Because induced gamma oscillations are not fixed at a definite point in time post- stimulus, analysis of averaged EEG data with traditional methods may result in an attenu- ated gamma burst power. We used a data alignment technique to improve the averaged data, making it a better representation of the individual induced EEG gamma oscillations. A study was designed to test the response of a subject to emotional stimuli, presented in the form of emotional facial expression images. In a four-part experiment, the subjects were instructed to identify gender in the first two blocks of the test, followed by differentiating between basic emotions in the final two blocks (i.e., anger vs. disgust). EEG data were collected from ASD (n ¼ 10), ADHD (n ¼ 9), and control (n ¼ 11) subjects via a 128-channel EGI system, and processed through a continuous wavelet transform and bandpass filter to isolate the gamma frequencies. A custom MATLAB code was used to align the data from individual trials between 200 and 600 ms poststimulus, EEG site, and condition by maximiz- ing the Pearson product–moment correlation coefficient between trials. The gamma power for the 400-ms window of maximum induced gamma burst was then calculated and com- pared between subject groups. Condition (anger/disgust recognition, gender recogni- tion)  Alignment  Group (ADHD, ASD, Controls) interaction was significant at most of parietal topographies (e.g., P3-P4, P7-P8). These interactions were better manifested in the aligned data set. Our results show that alignment of the induced gamma oscillations improves sensitivity of this measure in differentiation of EEG responses to emotional facial stimuli in ADHD and ASD. Received 27 December 2011; accepted 16 February 2012. Funding for this work was provided by the National Institutes for Health grant R01 MH86784 to Manuel Casanova. Address correspondence to Estate (Tato) M. Sokhadze, PhD, Cognitive Neuroscience Laboratory, Department of Psychiatry & Behavioral Sciences, University of Louisville School of Medicine, 401 East Chestnut Street, Suite 600, Louisville, KY 40202, USA. E-mail: [email protected] 78 INDUCED EEG GAMMA ALIGNMENT IN FACIAL TASK 79 INTRODUCTION gamma frequencies, particularly those cen- tered about 40 Hz, have been tied to visual, Autism spectrum disorder (ASD) and attention attentional, cognitive, and memory processes deficit=hyperactivity disorder (ADHD) are both (Bas¸ar, Schu¨rmann, Bas¸ar-Eroglu, & Demiralp, early onset neurodevelopmental disorders. 2001). Following a stimulus, two gamma oscil- Children with autism are typically character- lations are typically noted: an early evoked ized by social and emotional deficits stemming oscillation and a late induced oscillation from an inability to properly perceive and (Bas¸ar-Eroglu, Stru¨ber, Schu¨rmann, Stadler, & respond to these forms of stimuli. In children Bas¸ar, 1996; Bas¸ar, Schu¨rmann, Bas¸ar-Eroglu, with ADHD, attentional, impulse, and motor & Demiralp, 2001). The evoked gamma oscil- controls are notably decreased. Although most lations typically occur within the first 200 ms studies have typically separated these con- after the onset of a stimulus and are locked ditions as unrelated phenomena, more recent reviews have justified the comparison of these in time from trial to trial. Because little variation disorders in a combined experiment, which is seen in the latency of the evoked gamma may potentially reveal mechanistic differences with changing stimulus type, it is believed that between similar behaviors in ASD and ADHD it may be a result of sensory processes. Con- (Rommelse, Geurts, Franke, Buitelaar, & versely, induced gamma oscillations occur Hartman, 2011). later, after 240-ms poststimulus and vary in The theory of mind (ToM) represents the latency from trial to trial (Tallon-Baudry & attempt of assuming another’s perspective by Bertrand, 1999). These variations may suggest characterizing their mental state, or comparing that the induced gamma oscillations are related it to one’s own (Baron-Cohen, 2000; to higher cognitive processes (Tallon-Baudry, Baron-Cohen & Belmonte, 2005). The ToM 2003). Deviations from typical gamma band construct is frequently applied in the study of activity have been reported in several studies ASD (Ahmed & Miller, 2011; Lerner, Hutchins, on neurological and psychiatric disorders, inc- & Prelock, 2011; Sabbagh, 2004) and may luding epilepsy, Alzheimer’s disease, ADHD, explain why autistic children struggle with and ASD (Herrmann & Demiralp, 2005). understanding facial expressions, body lan- Because evoked gamma waveforms are guage, figurative speech, and other social cues synchronized in time poststimulus, averaging that convey emotional information. Applica- analogous trials typically reveals the evoked tions of this theory have been used to assess response in the averaged waveform. However, both the nature and level of emotional defi- induced gamma waveforms vary in time, and ciencies in adults with ASD (Baron-Cohen, thus are not typically represented in the aver- Wheelwright, & Jolliffe, 1997). Applying ToM aged signal. This makes the analysis of the to other conditions, such as ADHD (Buhler, induced gamma waveform more complex than Bachmann, Goyert, Heinzel-Gutenbrunner, & evoked gamma waveforms. Thus, studies look- Kamp-Becker, 2011), may provide a perspec- ing at averaged oscillatory gamma waveforms tive that allows for a better understanding of have either focused on evoked gamma (Lenz the disorder. et al., 2008) or used time-frequency analysis A quantitative analysis of the electroence- to find and characterize induced gamma phalographic (EEG) data during a ToM task in activity (Mu¨ller, Gruber, & Keil, 2000). subjects with neurodevelopmental disorders Data alignment is a procedure that corre- (e.g., ASD, ADHD) provides a top-down lates analogous features between two signals approach to better correlate physiological and or images, and standardizes them